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Using Explainable Machine Learning to Identify Determinants of Spinal Deformities in Children: It's Not Only About
Dragica Bukumirić1, Aleksandra Ilić2, Mirjana Pajčin2
1Institute of Public Health of Serbia "Dr Milan Jovanovic Batut", 11000 Belgrade, Serbia.
Healthcare (Basel, Switzerland)
|June 26, 2026
Summary
Pes planus (flat feet) is a key factor in childhood spinal deformities, alongside age and chronic illness. Early screening for flat feet can aid prevention strategies for these common conditions.
Area of Science:
- Pediatric Orthopedics
- Public Health
- Data Science
Background:
- Spinal deformities in children are a significant public health concern with potential long-term impacts.
- Early identification of contributing factors is crucial for effective prevention strategies.
Purpose of the Study:
- To identify key determinants of spinal deformities in children using advanced machine learning techniques.
- To leverage explainable AI for interpreting the influence of various factors on spinal health.
Main Methods:
- Secondary analysis of the 2019 Serbian National Health Survey data (n=1309 children, aged 5-14).
- Utilized logistic regression with LASSO, multiple ML algorithms, selecting XGBoost as the optimal model.
- Addressed class imbalance with class weighting and SMOTE; employed SHAP analysis for model interpretability.
Main Results:
- Prevalence of spinal deformities was 8.6%.
- Pes planus, age, and chronic illness were identified as significant determinants.
- XGBoost model demonstrated robust performance and interpretability via SHAP analysis, highlighting pes planus as the strongest predictor.
Conclusions:
- Explainable AI, specifically XGBoost with SHAP, effectively identifies and interprets spinal deformity determinants in children.
- Pes planus is a significant, modifiable determinant, underscoring its importance in pediatric screening and prevention programs.
